Fused Lasso is a popular regression technique that encodes the smoothness of the data. It
has been applied successfully to many applications with a smooth feature structure …

Solving Generalized LASSO (GL) problems is challenging, particularly when analyzing
many features with a complex interacting structure. Recent developments have found …

In order to solve large-scale lasso problems, screening algorithms have been developed
that discard features with zero coefficients based on a computationally efficient screening …

The fused Lasso penalty enforces sparsity in both the coefficients and their successive
differences, which is desirable for applications with features ordered in some meaningful …

Lasso is a widely used regression technique to find sparse representations. When the
dimension of the feature space and the number of samples are extremely large, solving the …

As a popular method in machine learning, lasso performs regression and feature selection
simultaneously. However, for large datasets, the training efficiency of lasso remains a …

In many statistical modeling problems, such as classification and regression, it is common to
encounter sparse and blocky coefficients. Sparse fused Lasso is specifically designed to …

Most state-of-the-art feature selection methods tend to overlook the structural relationship
between a pair of samples associated with each feature dimension, which may encapsulate …

Sparse learning techniques have been routinely used for feature selection as the resulting
model usually has a small number of non-zero entries. Safe screening, which eliminates the …

LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the
shrinkage and variable selection simultaneously. Since LASSO uses the L 1 penalty, the …